Person:
Payan, Alexia P.

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Now showing 1 - 10 of 14
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    Use of Machine Learning to Create a Database of Wires for Helicopter Wire Strike Prevention
    (Georgia Institute of Technology, 2021-01-04) Harris, Caleb M. ; Achour, Gabriel ; Payan, Alexia P. ; Mavris, Dimitri N. ; Daniel Guggenheim School of Aerospace Engineering ; Aerospace Systems Design Laboratory (ASDL) ; College of Engineering
    Rotorcraft collisions with wires and power lines have been a major cause of accidents over the past decades. They are rather difficult to predict and often result in fatalities. For this reason, there is a push to provide pilots with additional information regarding wires in the surrounding environment of the helicopter. However, the precise locations of power lines and other aerial wires are not available in any centralized database. This work proposes the development of a wire database in two phases. First, power line structures are detected from aerial imagery using deep learning techniques. Second, the complete power grid network is predicted using a centralized many-to-many graph search. The two-step framework produces an approximate medium-voltage grid stored as a set of connected line segments in GPS coordinates. Experiments are conducted in Washington D.C. using openly available datasets. Results show that utility pole locations can be predicted from satellite imagery using deep learning methods and a full grid network can be generated to a level of detail depending on computational power and available data for inference in the graph search. Even with limited computational resources and a noisy dataset, over a a fourth of the grid network is directly predicted within a range of seven meters, and the majority of the network is visually inferred from nearby detections. Moving forward, the goal is to apply the proposed framework to larger regions of the U.S., with rural and urban environments, to map all wires and cables that are a threat to rotorcraft safety.
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    Emergency Planning for Aerial Vehicles by Approximating Risk with Aerial Imagery and Geographic Data
    (Georgia Institute of Technology, 2022-01) Harris, Caleb M. ; Kim, Seulki ; Payan, Alexia P. ; Mavris, Dimitri N. ; Daniel Guggenheim School of Aerospace Engineering ; Aerospace Systems Design Laboratory (ASDL) ; College of Engineering
    Urban Air Mobility and Advanced Air Mobility require the certification of novel electrified, vertical takeoff and landing, and autonomous aerial vehicles. These vehicles will operate at lower altitudes, in more dense environments, and with limited recovery abilities. Therefore, emergency landing scenarios must be considered broadly to understand the risks in some situations of flight failures. This work provides a preflight planning tool to assist these vehicles when emergency landing is required in crowded environments by fusing geographic data about the population, geometric data from lidar scans, and semantic data about land cover from aerial imagery. The Pix2Pix Conditional GAN is trained on Washington D.C. datasets to predict eight classifications at a 1m resolution. The information from this detection phase is transformed into a costmap, or riskmap, to use in planning the path to the safest landing locations. Multiple combinations of the cost layers are investigated in three test scenarios. The Rapidly Exploring Random Tree (RRT) algorithm efficiently searches for an alternative path that minimizes risk during emergency landing. The tool is demonstrated through three scenarios in the D.C. area. The objective is that the tool allows for the safe operation of UAM and AAM vehicles through crowded regions by providing confidence to the local population and federal regulators.
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    Decision-Making and Optimization Framework for the Design of Emerging Satellite Constellations
    (AIAA, 2023-01) Koerschner, Marc A. ; Krishnan, Kavya Navaneetha ; Payan, Alexia P. ; Mavris, Dimitri N. ; Daniel Guggenheim School of Aerospace Engineering ; Aerospace Systems Design Laboratory (ASDL) ; College of Engineering
    With the parallel increase in global orbital debris due to passive object collisions, as well as in the number of proposed low earth orbit mega-constellations, in anti-satellite missile tests, and the fielding of new satellites, there is an inherent need for a framework to optimize the design of Low Earth Orbit (LEO) mega-constellations to avoid collisions while maintaining the functionality of the constellation. In this paper, we aim to provide a framework that unifies these considerations in the conceptual design phase of mega-constellations. We start with a discussion of metrics of importance for the design of mega-constellations, namely coverage, collision risk, collision avoidance, and station-keeping costs. With these metrics defined, we utilize the first principles of orbital mechanics and statistical models to analyze potential alternative mega-constellation designs. These designs are then optimized using Non-denominated Sorting Genetic Algorithm 2 (NSGA2) with our own defined objective function to create a repository of Pareto optimal configurations. We then showcase how a multi-criteria decision-making methodology can be utilized by a variety of unique stakeholders and subject-matter experts to select an optimal constellation design for a given scenario. A Pareto Frontier collection with optimal solutions of 10 constellations was produced by the framework. Radar plots to assess the significance of the weighted metric of the framework shows several trading options for conceptual designs of the constellations. We finally discuss the scope, limitations, applications, and future work for various scenarios.
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    Framework for Multi-Asset Comparison and Rapid Down-selection for Earth Observation Missions
    (Georgia Institute of Technology, 2019) Gilleron, Jerome ; Muehlberg, Marc ; Payan, Alexia P. ; Choi, Youngjun ; Briceno, Simon ; Mavris, Dimitri N. ; Daniel Guggenheim School of Aerospace Engineering ; Aerospace Systems Design Laboratory (ASDL) ; College of Engineering
    Observing the Earth, whether it be from space or from the air, has become easier in recent years with the advent of new space-borne and airborne technologies. First, satellites constantly provide data about almost any point on the globe with varying resolutions and in various spectral bands. Second,Unmanned Aerial Vehicles (UAV) are becoming more readily accessible to the public and may be rapidly deployed to take high resolution images of ground features or areas of interest. Third, manned aircraft may be used to image large areas of land at a higher resolution than satellites and have been used regularly in disaster monitoring and surveillance missions. However, when multiple heterogeneous assets compete to perform a given aerial imaging mission, deciding which asset is better suited and/or less costly to operate in a timely manner is challenging. Every acquisition mode is different, resolution values are computed differently and there currently does not exist a common framework to compare UAV, manned aircraft and satellites. To address this challenge, this paper describes a methodology to rapidly compare various types of aerial assets (such as UAVs and manned aircraft) and space assets (such as satellites) to decide which one would be better able to perform an Earth observation mission depending on a set of requirements. To demonstrate the proposed methodology, this paper executes numerical simulations with three different representative scenarii in California.
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    Analysis of Weather-Related Helicopter Accidents and Incidents in the United States
    (Georgia Institute of Technology, 2021-08) Ramee, Coline ; Speirs, Andrew H. ; Payan, Alexia P. ; Mavris, Dimitri N. ; Daniel Guggenheim School of Aerospace Engineering ; Aerospace Systems Design Laboratory (ASDL) ; College of Engineering
    Helicopters typically operate at lower altitudes than fixed-wing aircraft and can take-off and land away from airports. Thus, helicopter pilots have decreased access to weather information due to connectivity issues or sparsity of weather coverage in those areas and at those altitudes. Moreover, regulations allow most rotorcraft to operate in marginal weather conditions. Therefore, weather is a challenge to rotorcraft operations. In this study, rotorcraft events in the United States between 2008 and 2018 in which weather was determined to be a factor are analyzed using the National Transportation Safety Board aviation database. Results show that weather was a factor in 28% of rotorcraft fatal accidents. Wind was involved in most incidents but more rarely involved in fatalities. Bad visibility conditions due to a combination of low illumination and clouds were responsible for most fatal weather-related accidents. Personal flights had the highest accident and incident rates. Finally, the Helicopter Air Ambulance industry had the largest number of incidents and accidents related to visibility conditions out of all other industries. The authors recommend improving awareness of the conditions in which weather events occur and improving training to maintain control of the aircraft in windy conditions or during inadvertent instrument meteorological conditions.
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    Impact of Adverse Weather on Commercial Helicopter Pilot Decision-Making and Standard Operating Procedures
    (Georgia Institute of Technology, 2021-08) Speirs, Andrew H. ; Ramee, Coline ; Payan, Alexia P. ; Mavris, Dimitri N. ; Feigh, Karen M. ; Daniel Guggenheim School of Aerospace Engineering ; Aerospace Systems Design Laboratory (ASDL) ; College of Engineering
    Helicopter pilots face unique challenges with regard to adverse weather when compared to fixed-wing pilots. Rotorcraft typically operate at lower altitudes in off-field areas that are not always well covered by weather reporting stations. Although recent technological advances have increased the amount of weather data that pilots can access in the cockpit, weather remains a factor in 28% of fatal helicopter accidents. In this work, commercial helicopter pilots were surveyed and interviewed to better understand how they gather and process weather information, what the perceived limitations of current weather tools are, and how their decision-making process is affected by the information they gather and/or receive. Pilots were found to use a wide variety of weather sources for their initial go or no-go decision during the preflight phase, but use fewer weather sources in the cockpit while in-flight. Pilots highlighted the sparsity and sometimes inaccuracy of the weather information available to them in their prototypical operational domain. To compensate, they are forced to rely on local and experiential weather knowledge to supplement weather reports while still working to mitigate other external pressures. Based on the literature and on results from this work, recommendations are made to address the weather-related gaps faced by the rotorcraft community. This includes the installation of additional weather reporting stations outside of airports and densely populated areas, the further promotion of the HEMS tool to helicopter pilots in all industries, the development of weather tools capable of visualizing light precipitation such as fog, and the development of in-flight graphical displays that can help reduce the cognitive workload of interpreting weather information.
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    Optimal Siting of Sub-Urban Air Mobility (sUAM) Ground Architectures using Network Flow Formulation
    (Georgia Institute of Technology, 2020-06) Venkatesh, Nikhil ; Payan, Alexia P. ; Justin, Cedric Y. ; Kee, Ethan C. ; Mavris, Dimitri N. ; Daniel Guggenheim School of Aerospace Engineering ; Aerospace Systems Design Laboratory (ASDL) ; College of Engineering
    Air Mobility (AM) operating models have steadily made their way into public conscience over the past decade due to increased research activity pioneered by large technology corporations such as Uber and Amazon. Estimates concur that there are around 250 startup businesses with 22 major players working on such technologies with over $25 billion dollars in venture capital funding in 2017[1]. Given the meteoric rise of Air Mobility as one of the leading 21st century disruptive technologies, research effort across the spectrum of functions that can make AM concepts a reality are burgeoning - ranging from vehicle design to operations planning. More specifically, research efforts within the operations planning space deal with service route identification, ground infrastructure (such as charging stations and ports) placement and others. To this effect, the present study seeks to evaluate the feasibility and tractability of a formalized optimization method towards the siting of "vertiports" - ground infrastructure that aids the embarkation and disembarkation of AM commuters - as applied to a Sub-Urban Air Mobility (sUAM) operating model. Mixed-Integer Programming (MIP) formulations offer qualified benefits over other heuristic methods and the authors are confident of their relative performance given the proven track record of such methods in solving generalized facility location problems (GFLP). In this study, two optimization problems were considered: capacitated vertiport siting, where any vertiport considered would need to adhere to capacity constraints; and uncapacitated vertiport siting, where any vertiport considered does not have any capacity limit and can service unlimited demand. Results indicate that a network flow formulation using an MIP methodology is able to adequately place vertiports for sUAM business operations to satisfy demand flows associated with home-work commute.
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    Helicopter Operations Weather Information Survey Dataset
    (Georgia Institute of Technology, 2020-11-23) Payan, Alexia P. ; Ramee, Coline ; Speirs, Andrew ; Mavris, Dimitri N. ; Feigh, Karen M. ; Daniel Guggenheim School of Aerospace Engineering ; College of Engineering
    To better understand the kind of weather information used by rotorcraft operators and get their opinion on the weather products that are available to them, the research team created an online survey. The survey consisted of three main sections: 1) Demographics, 2) Flight environment, and 3) Safety Operations. The information collected was used to analyze the number and types of weather information sources used by pilots in different phases of flight, identify differences between industries and study pilots training for adverse weather conditions. The data contained here is an anonymized version of answers to the survey.
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    Machine Learning Enabled Turbulence Prediction Using Flight Data for Safety Analysis
    (International Council of the Aeronautical Sciences (ICAS), 2021-09) Emara, Mariam ; dos Santos, Marcos ; Chartier, Noah ; Ackley, Jamey ; Puranik, Tejas G. ; Payan, Alexia P. ; Kirby, Michelle R. ; Pinon, Olivia J. ; Mavris, Dimitri N. ; Daniel Guggenheim School of Aerospace Engineering ; Aerospace Systems Design Laboratory (ASDL) ; College of Engineering
    The hazards posed by turbulence remain an important issue in commercial aviation safety analysis. Turbulence is among the leading cause of in-flight injury to passengers and flight attendants. Current methods of turbulence detection may suffer from sparse or inaccurate forecast data sets, low spatial and temporal resolution , and lack of in-situ reports. The increased availability of flight data records offers an opportunity to improve the state-of-the-art in turbulence detection. The Eddy Dissipation Rate (EDR) is consistently recognized as a reliable measure of turbulence and is widely used in the aviation industry. In this paper, both classification and regression supervised machine learning models are used in conjunction with flight operations quality assurance (FOQA) data collected from 6,000 routine flights to estimate the EDR (and thereby turbulence severity) in future time horizons. Data from routine airline operations that encountered different levels of turbulence is collected and analyzed for this purpose. Results indicate that the models are able to perform reasonably well in predicting the EDR and turbulence severity around 10 seconds prior to encountering a turbulence event. Continuous deployment of the model enables obtaining a near-continuous prediction of possible future turbulence events and builds the capability towards an early warning system for pilots and flight attendants.
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    Knowledge Discovery Within ADS-B Data from Routine Helicopter Tour Operations
    (Georgia Institute of Technology, 2020-06) Chin, Hsiang-Jui ; Payan, Alexia P. ; Mavris, Dimitri N. ; Johnson, Charles C. ; Daniel Guggenheim School of Aerospace Engineering ; Aerospace Systems Design Laboratory (ASDL) ; College of Engineering
    Knowledge discovery or data mining techniques are widely used for anomaly detection in the commercial aviation domain to retrospectively improve operational safety. However, in the general aviation domain, especially for rotorcraft, analyses of flight data records for anomaly detection are not as prevalent. In this study, ADS-B data from a helicopter tour operator will be used to develop a prototype framework for uncovering patterns from routine flights. The ADS-B data contains two types of information: 1) time series of various flight parameters and 2) trajectory parameters. Various knowledge discovery techniques able to handle the aforementioned data types are explored and a few promising methods are applied to the ADS-B data of a helicopter tour operator in Hawaii. From the clustering results, patterns in the flight data records can be observed and can then be used by Subject-Matter Experts (SMEs) to facilitate the detection of anomalies. With this framework in place, rotorcraft operators will be able to analyze their routine flight data to not only monitor the safety of their operations but also to acquire knowledge on their operational patterns.